1. Introduction

This notebook is to explore the data collected by ABC Bank to predict customer churn.

2. Imports

#Importing necessary packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas_profiling

#importing data
churn_data=pd.read_csv("../raw data/Churn_data.csv")

#Preview of data
churn_data.head()

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	1	15634602	Hargrave	619	France	Female	42	2	0.00	1	1	1	101348.88	1
1	2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0
2	3	15619304	Onio	502	France	Female	42	8	159660.80	3	1	0	113931.57	1
3	4	15701354	Boni	699	France	Female	39	1	0.00	2	0	0	93826.63	0
4	5	15737888	Mitchell	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0

#Reviewing data types
churn_data.dtypes

RowNumber            int64
CustomerId           int64
Surname             object
CreditScore          int64
Geography           object
Gender              object
Age                  int64
Tenure               int64
Balance            float64
NumOfProducts        int64
HasCrCard            int64
IsActiveMember       int64
EstimatedSalary    float64
Exited               int64
dtype: object

The data has 13 independant features of various data types (int,float,object) and the exited column is the dependant feature of interest.

The HasCrCard and IsActiveMember are formatted as int64, but these are categorical features, so we should convert it into objects.

The dependant feature is formatted as int64, but we might want to convert it into object to make this a classification problem.

The customer name feature might not be of much use. This could be discarded later.

#Converting into correct formats
churn_data["Geography"]=churn_data["Geography"].astype("category")
churn_data["Gender"]=churn_data["Gender"].astype("category")
churn_data["HasCrCard"]=churn_data["HasCrCard"].astype("category")
churn_data["IsActiveMember"]=churn_data["IsActiveMember"].astype("category")
churn_data["Exited"]=churn_data["Exited"].astype("category")
churn_data.dtypes

RowNumber             int64
CustomerId            int64
Surname              object
CreditScore           int64
Geography          category
Gender             category
Age                   int64
Tenure                int64
Balance             float64
NumOfProducts         int64
HasCrCard          category
IsActiveMember     category
EstimatedSalary     float64
Exited             category
dtype: object

#Reviewing data info
churn_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 14 columns):
 #   Column           Non-Null Count  Dtype   
---  ------           --------------  -----   
 0   RowNumber        10000 non-null  int64   
 1   CustomerId       10000 non-null  int64   
 2   Surname          10000 non-null  object  
 3   CreditScore      10000 non-null  int64   
 4   Geography        10000 non-null  category
 5   Gender           10000 non-null  category
 6   Age              10000 non-null  int64   
 7   Tenure           10000 non-null  int64   
 8   Balance          10000 non-null  float64 
 9   NumOfProducts    10000 non-null  int64   
 10  HasCrCard        10000 non-null  category
 11  IsActiveMember   10000 non-null  category
 12  EstimatedSalary  10000 non-null  float64 
 13  Exited           10000 non-null  category
dtypes: category(5), float64(2), int64(6), object(1)
memory usage: 752.6+ KB

The dataset contains 10000 rows of data and there are no missing values present. So, we can continue with our data exploration

#Reviewing data range for numercial variables
churn_data.describe()

	RowNumber	CustomerId	CreditScore	Age	Tenure	Balance	NumOfProducts	EstimatedSalary
count	10000.00000	1.000000e+04	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000
mean	5000.50000	1.569094e+07	650.528800	38.921800	5.012800	76485.889288	1.530200	100090.239881
std	2886.89568	7.193619e+04	96.653299	10.487806	2.892174	62397.405202	0.581654	57510.492818
min	1.00000	1.556570e+07	350.000000	18.000000	0.000000	0.000000	1.000000	11.580000
25%	2500.75000	1.562853e+07	584.000000	32.000000	3.000000	0.000000	1.000000	51002.110000
50%	5000.50000	1.569074e+07	652.000000	37.000000	5.000000	97198.540000	1.000000	100193.915000
75%	7500.25000	1.575323e+07	718.000000	44.000000	7.000000	127644.240000	2.000000	149388.247500
max	10000.00000	1.581569e+07	850.000000	92.000000	10.000000	250898.090000	4.000000	199992.480000

#Reviewing categorical variables
churn_data.describe(include=['category'])

	Geography	Gender	HasCrCard	IsActiveMember	Exited
count	10000	10000	10000	10000	10000
unique	3	2	2	2	2
top	France	Male	1	1	0
freq	5014	5457	7055	5151	7963

#Get categories of independant variables
print(churn_data.Geography.value_counts())
print(churn_data.Gender.value_counts())
print(churn_data.HasCrCard.value_counts())
print(churn_data.IsActiveMember.value_counts())

France     5014
Germany    2509
Spain      2477
Name: Geography, dtype: int64
Male      5457
Female    4543
Name: Gender, dtype: int64
1    7055
0    2945
Name: HasCrCard, dtype: int64
1    5151
0    4849
Name: IsActiveMember, dtype: int64

#Get categories of dependant variables
print(churn_data.Exited.value_counts())

0    7963
1    2037
Name: Exited, dtype: int64

Insights:

The customers are spread across 3 countries (France, Germany and Spain)
The male and female customers are uniformly distributed
About 70% of customers have credit card
50% of customers are active members

About 20% of customers have churned

profile_report = churn_data.profile_report(explorative=False, html={'style': {'full_width': True}})
profile_report